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Linear Modeling to Reduce Bias in Plastic Surgery Residency Selection.

Shady Elmaraghi1, Venkat K Rao1, Brian M Christie1

  • 1From the Department of Surgery, Division of Plastic Surgery, University of Wisconsin School of Medicine and Public Health; and the Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine.

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Linear modeling improved plastic surgery residency selection by using quantifiable criteria for ranking applicants. This data-driven approach enhances fairness and consistency in the application process.

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Area of Science:

  • Medical Education
  • Surgical Residency Selection
  • Quantitative Analysis in Healthcare

Background:

  • Residency selection aims to identify high-quality applicants consistently.
  • Traditional methods involve subjective scoring and ordinal ranking.
  • Linear modeling offers a data-driven approach supported by psychological research.

Purpose of the Study:

  • To evaluate the implementation of linear modeling in plastic surgery residency selection.
  • To assess the impact of a quantifiable ranking system on fairness and consistency.

Main Methods:

  • The University of Wisconsin Plastic Surgery Residency Program utilized linear modeling for the 2019 application cycle.
  • A model incorporated scores from United States Medical Licensing Examination (USMLE) Steps 1 and 2, letters of recommendation, publications, and extracurricular activities.
  • Applicant scores were calculated out of a maximum of 100.

Main Results:

  • The study compared a linear model-derived rank list with the program's traditional intuitive scoring method.
  • The Spearman rank correlation coefficient between the two lists was 0.532.
  • Discrepancies between the lists informed final ranking discussions.

Conclusions:

  • This study represents the first known use of linear modeling to enhance consistency, fairness, and reduce bias in plastic surgery residency selection.
  • Sharing this methodology can assist other programs in optimizing their ranking processes.
  • The approach assures applicants of evaluation based on objective, quantifiable criteria.